Building integrations is a pain, but the real struggle comes after.
How long did your last integration project take? Our guess is too long.
Why? Everyone creates their own architecture to solve this problem which works only for the first few integrations, after dozens, you hit a wall.
Every integration handles authentication, token refreshes, pagination, response schema and data modeling differently.
Over our careers we've run into these problems over and over again, which is why we built Beetl, so you don't have to.
Your systems, connected once.
We read what you already run, turn it into data that agrees with itself, and put it where your people actually work.
- Source systems: On-premise systems, Cloud APIs, Database, Files & SFTP.
- Beetl Connect, reads, never writes: Authentication & tokens, Pagination, rate limits, Schema changes, Source-faithful copy.
- Beetl Transform, explicit, versioned sql: SQL you can read, Joins across sources, Mapped to your terms, Versioned, not hidden.
- Beetl Deliver, validated output: On your cadence, Failures always visible, Reusable, not rebuilt.
- Where it lands: Chat, BI dashboards, Data warehouse, Files & SFTP.
Learn more about how the Beetl platform works.
Neither system could answer this on its own.
The quality measurements live in one system. The record of which tool made which part lives in another. Apart, each is a list nobody reads. Joined and kept current, they show which tools are wearing faster than their peers and how close each one is to the limit it was meant to stay inside.

Where we start.
Customers keep asking us to integrate with systems we don't support
We build and run every integration your customers ask for, so your engineers stay on your product instead of becoming an integration team. Deals stop stalling on a connector you have not written yet.
Embedded integrationsOur data is stuck across systems that don't work together
We connect what you already run and keep the data flowing between it, so the numbers line up long before the systems do. An ERP migration, an acquisition, or a monthly report stops waiting on work nobody owns.
See the three shapes this takesWe built this once, for ourselves.
The Beetl founding team rebuilt the data platform behind a programmatic job ads product at an HR tech company. The architecture we landed on is the architecture Beetl runs on today.
The problem was never the pipeline. It was proving the pipeline was wrong.
An HR tech company of 1,200 people. 6 data engineers ran the pipeline behind a programmatic job ads platform processing billions of events a day. Most of their capacity went into one loop: is this number actually wrong, or is the metric behaving exactly as designed? Days to reproduce it. Days more to backfill it.
We started with fixing ingestion. One source, done properly.
Everything downstream depends on what comes in, so nothing further along the pipeline can rescue bad data. Starting with the highest impact fix is still the shape of a first engagement with Beetl.
Then we rebuilt the floor underneath it.
Declarative SQL pipelines on a Rust runtime, reading and writing files in object storage instead of a database. With lower infrastructure costs and higher reliability, debugging stopped eating valuable team capacity, which could now go into data products the platform had never been able to offer.
That company was not a Beetl customer. Beetl did not exist yet. These are numbers we measured on a system the three of us built and operated ourselves, and they are the reason Beetl is built the way it is.
Tell us what you run.
Name the systems and what has to come out of them. We will tell you what we can connect, what it would take, and honestly if it is not a fit.
Start with the one that costs you the most.
Tell us what breaks, what it feeds, and who notices when it goes wrong. We hit this problem ourselves over and over again throughout our careers, which is why Beetl exists. Vienna-based, hosted in the EU.